{"ID":2886187,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.03174","arxiv_id":"2508.03174","title":"InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation","abstract":"Collaborative partnership matters in inquiry-oriented education. However, most study partners are selected either rely on experience-based assignments with little scientific planning or build on rule-based machine assistants, encountering difficulties in knowledge expansion and inadequate flexibility. This paper proposes an LLM-empowered agent model for simulating and selecting learning partners tailored to inquiry-oriented learning, named InqEduAgent. Generative agents are designed to capture cognitive and evaluative features of learners in real-world scenarios. Then, an adaptive matching algorithm with Gaussian process augmentation is formulated to identify patterns within prior knowledge. Optimal learning-partner matches are provided for learners facing different exercises. The experimental results show the optimal performance of InqEduAgent in most knowledge-learning scenarios and LLM environment with different levels of capabilities. This study promotes the intelligent allocation of human-based learning partners and the formulation of AI-based learning partners. The code, data, and appendix are publicly available at https://github.com/InqEduAgent/InqEduAgent.","short_abstract":"Collaborative partnership matters in inquiry-oriented education. However, most study partners are selected either rely on experience-based assignments with little scientific planning or build on rule-based machine assistants, encountering difficulties in knowledge expansion and inadequate flexibility. This paper propos...","url_abs":"https://arxiv.org/abs/2508.03174","url_pdf":"https://arxiv.org/pdf/2508.03174v3","authors":"[\"Wen-Xi Yang\",\"Tian-Fang Zhao\",\"Guan Liu\",\"Liang Yang\",\"Zi-Tao Liu\",\"Wei-Neng Chen\"]","published":"2025-08-05T07:33:48Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\"]","has_code":false,"code_links":[{"ID":611285,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2886187,"paper_url":"https://arxiv.org/abs/2508.03174","paper_title":"InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation","repo_url":"https://github.com/InqEduAgent/InqEduAgent","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}